Utilizing machine learning models to automatically perform actions that maintain a plan for an event

ABSTRACT

A device receives, from a user device, plan information that identifies a plan for an event and includes information identifying an account associated with the plan, plan items of the plan, and priorities and preferences associated with the plan items, where the user device is associated with a user of the account and the plan. The device receives transaction information identifying transactions associated with the account, and processes the plan information and the transaction information, with a first model, to identify transactions related to the plan. The device processes information associated with the particular plan item, the plan information, and the transaction information, with a second model, to determine recommendations for the plan, where the information associated with the particular plan item includes information identifying a priority and a preference associated with the particular plan item. The device provides information indicating the recommendations to the user device.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/523,087, filed Jul. 26, 2019 (now U.S. Pat. No. 11,138,674), which isa continuation of U.S. patent application Ser. No. 16/280,675, filedFeb. 20, 2019 (now U.S. Pat. No. 10,410,294), which are incorporatedherein by reference.

BACKGROUND

A personal plan or a home plan is a plan that allocates future personalincome towards expenses, savings, debt repayment, and/or the like. Pastspending and personal debt may be considered when creating a personalplan. A person may create a personal plan for one or more events. Forexample, a person may create a monthly plan (e.g., where a month is theevent) to plan for costs associated with housing, food, transportation,and/or the like. In another example, a person may create a plan for atrip (e.g., a business trip, a vacation, and/or the like) to plan forcosts associated with lodging, food, travel, entertainment, and/or thelike.

SUMMARY

According to some implementations, a method may include receiving planinformation identifying a plan for an event, wherein the planinformation includes information identifying an account associated withthe plan, plan items of the plan, priorities associated with the planitems, and preferences associated with the plan items. The method mayinclude receiving, after receiving the plan information, transactioninformation identifying transactions associated with the account, andprocessing the plan information and the transaction information, with afirst machine learning model, to identify transactions related to theplan. The method may include determining that a threshold preference fora particular plan item of the plan is satisfied based on one or more ofthe transactions related to the plan. The method may include processinginformation associated with the particular plan item, the planinformation, and the transaction information, with a second machinelearning model, to determine one or more recommendations for the plan,wherein the information associated with the particular plan itemincludes information identifying a priority associated with theparticular plan item, and a preference associated with the particularplan item. The method may include automatically performing one or moreactions based on the one or more recommendations for the plan.

According to some implementations, a device may include one or morememories, and one or more processors, communicatively coupled to the oneor more memories, to receive, from a user device, plan informationidentifying a plan for an event, wherein the plan information includesinformation identifying an account associated with the plan, plan itemsof the plan, priorities associated with the plan items, and preferencesassociated with the plan items, and wherein the user device isassociated with a user of the account and the plan. The one or moreprocessors may receive, after receiving the plan information,transaction information identifying transactions associated with theaccount, and process the plan information and the transactioninformation, with a first model, to identify transactions related to theplan. The one or more processors may determine that a thresholdpreference for a particular plan item of the plan is satisfied based onone or more of the transactions related to the plan. The one or moreprocessors may process information associated with the particular planitem, the plan information, and the transaction information, with asecond model, to determine one or more recommendations for the plan,wherein the information associated with the particular plan itemincludes information identifying a priority associated with theparticular plan item, and a preference associated with the particularplan item, and provide information indicating the one or morerecommendations to the user device.

According to some implementations, a non-transitory computer-readablemedium may store instructions that include one or more instructionsthat, when executed by one or more processors of a device, cause the oneor more processors to receive plan information identifying a plan for anevent, wherein the plan information includes information identifying anaccount associated with the plan, plan items of the plan, prioritiesassociated with the plan items, and preferences associated with the planitems. The one or more instructions may cause the one or more processorsto receive, after receiving the plan information, transactioninformation identifying transactions associated with the account, andprocess the plan information and the transaction information, with afirst machine learning model, to identify transactions related to theplan. The one or more instructions may cause the one or more processorsto determine that a threshold preference for a particular plan item ofthe plan is satisfied based on one or more of the transactions relatedto the plan, and provide, to a user device associated with a user of theplan, an alert indicating that the particular plan item satisfies thethreshold preference. The one or more instructions may cause the one ormore processors to process information associated with the particularplan item, the plan information, and the transaction information, with asecond machine learning model, to determine one or more recommendationsfor the plan, wherein the information associated with the particularplan item includes information identifying a priority associated withthe particular plan item, and a preference associated with theparticular plan item.

The one or more instructions may cause the one or more processors toprovide, to the user device, information indicating the one or morerecommendations.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1G are diagrams of an example implementation described herein.

FIG. 2 is a diagram of an example environment in which systems and/ormethods, described herein, may be implemented.

FIG. 3 is a diagram of example components of one or more devices of FIG.2.

FIGS. 4-6 are flow charts of example processes for utilizing machinelearning models to automatically perform actions that maintain a planfor an event.

DETAILED DESCRIPTION

The following detailed description of example implementations refers tothe accompanying drawings. The same reference numbers in differentdrawings may identify the same or similar elements.

Maintaining a plan is very difficult when the plan includes unrealisticplan thresholds, the plan is not flexible enough to handle changingconditions (e.g., increased prices of products and/or services),emergency expenses occur, and/or the like. Furthermore, accounting forplan expenses during a specific event (e.g., a business trip) can becumbersome, require saving receipts for all expenditures, requirejustifying the expenditures, and/or the like.

Some implementations described herein provide a plan platform thatutilizes machine learning models to automatically perform actions thatmaintain a plan for an event. For example, the plan platform may receiveplan information identifying a plan for an event, where the planinformation includes information identifying an account associated withthe plan, plan items of the plan, priorities associated with the planitems, and preferences associated with the plan items. The plan platformmay receive, after receiving the plan information, transactioninformation identifying transactions associated with the account, andmay process the plan information and the transaction information, with afirst machine learning model, to identify transactions related to theplan. The plan platform may determine that a threshold preference for aparticular plan item of the plan is satisfied based on one or more ofthe transactions related to the plan, and may process informationassociated with the particular plan item, the plan information, and thetransaction information, with a second machine learning model, todetermine one or more recommendations for the plan, where theinformation associated with the particular plan item includesinformation identifying a priority associated with the particular planitem, and a preference associated with the particular plan item. Theplan platform may automatically perform one or more actions based on theone or more recommendations for the plan.

In this way, the plan platform may proactively act when a plan is beingviolated and may provide one or more recommendations to help stop theplan from being violated. The plan platform may also conserve resources(e.g., processing resources, memory resources, and/or the like)associated with maintaining a plan since the plan platform may simplifymaintenance of the plan. Furthermore, the plan platform may simplify theprocess of accounting for plan expenses during a specific event byautomatically tracking transactions (e.g., expenditures), justifying theexpenditures, and/or the like.

FIGS. 1A-1G are diagrams of an example implementation 100 describedherein. As shown in FIGS. 1A-1G, a user device may be associated with auser and a plan platform. As shown in FIG. 1A, the user of the userdevice may utilize the user device to define a plan for an event. Insome implementations, the plan may include a personal budget, a workbudget, a financial plan, a retirement plan, a savings plan, a spendingplan, and/or the like. In some implementations, the event may include arecurring time period (e.g., daily, weekly, monthly, and/or the like), avacation, a business trip, a transaction, college, and/or the like.

In some implementations, the user may utilize the user device to receivean application from the plan platform and may install the application onthe user device. The application may enable the user to utilize the userdevice to define the plan for the event, to provide the plan to the planplatform, to make changes to the plan, to receive recommendations (e.g.,from the plan platform) with respect to the plan, and/or the like.

As further shown in FIG. 1A, the user device may present, to the user, auser interface that enables the user to define the plan for the event.For example, the user interface may request information identifying theevent, an account associated with the user (e.g., from which funds areto be used for the event, an account associated with a transaction card,such as a credit card, a debit card, a gift card, a rewards card, and/orthe like), plan items (e.g., plan categories, such as travel,entertainment, lodging, food, and/or the like), priorities for the planitems (e.g., the travel category is more important than theentertainment category and funds may be used from the entertainmentcategory for the travel category), preferences for the plans (e.g.,amounts of funds allocated to the plan items, thresholds indicating whenfunds allocated to the plan items are almost depleted or are depleted,whether to receive alerts associated with the thresholds, and/or thelike), and/or the like. In some implementations, threshold preferencesfor plan items may be time sensitive. For example, if the user isplanning for a trip (e.g., with a $250 budget for a five-day trip, or$50 per day of the trip), the user may exceed a daily threshold (e.g.,$50 per day) for the trip based on current spending (e.g., the userspends $160 by the third day of the trip). In such an example, the planplatform may recommend that the user adjust an upcoming daily budget inorder to satisfy the trip budget (e.g., only spend $25 tomorrow).

As further shown in FIG. 1A, and by reference number 105, the planplatform may receive, from the user device, plan information for theevent (e.g., the information defined by the user via the userinterface). In some implementations, the plan platform may process theplan information, with a machine learning model, to identify recommendedplan items (e.g., if the user is traveling to a third world country, theplan platform may recommend a plan item for medication), recommendedpriorities for one or more plan items (e.g., if the user is traveling toa costly area, the plan platform may recommend prioritizing lodging andfood over other plan items), recommended preferences for one or moreplan items (e.g., the plan platform may recommend increasing a thresholdfor an entertainment category if the user values entertainment), and/orthe like. In some implementations, the plan platform may provideinformation identifying the recommended plan items, priorities,preferences, and/or the like to the user device while the user isdefining the plan.

In some implementations, the machine learning model may include a Markovmodel, a Bayesian model, a Bayesian-Markov decision model, and/or thelike that identifies recommended plan items, priorities, preferences,and/or the like. In some implementations, the plan platform may performa training operation on the machine learning model with historical planinformation (e.g., from prior plans of the user, from plans of othersimilar users, and/or the like).

The plan platform may separate the historical plan information into atraining set, a validation set, a test set, and/or the like. In someimplementations, the plan platform may train the machine learning modelusing, for example, an unsupervised training procedure and based on thehistorical plan information. For example, the plan platform may performdimensionality reduction to reduce the historical plan information to aminimum feature set, thereby reducing resources (e.g., processingresources, memory resources, and/or the like) to train the machinelearning model, and may apply a classification technique, to the minimumfeature set.

In some implementations, the plan platform may use a logistic regressionclassification technique to determine a categorical outcome (e.g.,identified plan items, preferences, priorities, and/or the like).Additionally, or alternatively, the plan platform may use a naiveBayesian classifier technique. In this case, the plan platform mayperform binary recursive partitioning to split the historical planinformation into partitions and/or branches, and use the partitionsand/or branches to perform predictions (e.g., identified plan items,priorities, preferences, and/or the like). Based on using recursivepartitioning, the plan platform may reduce utilization of computingresources relative to manual, linear sorting and analysis of datapoints, thereby enabling use of thousands, millions, or billions of datapoints to train the machine learning model, which may result in a moreaccurate model than using fewer data points.

Additionally, or alternatively, the plan platform may use a supportvector machine (SVM) classifier technique to generate a non-linearboundary between data points in the training set. In this case, thenon-linear boundary is used to classify test data into a particularclass.

Additionally, or alternatively, the plan platform may train the machinelearning model using a supervised training procedure that includesreceiving input to the machine learning model from a subject matterexpert, which may reduce an amount of time, an amount of processingresources, and/or the like to train the machine learning model ofactivity automatability relative to an unsupervised training procedure.In some implementations, the plan platform may use one or more othermodel training techniques, such as a neural network technique, a latentsemantic indexing technique, and/or the like. For example, the planplatform may perform an artificial neural network processing technique(e.g., using a two-layer feedforward neural network architecture, athree-layer feedforward neural network architecture, and/or the like) toperform pattern recognition with regard to patterns of the historicalplan information. In this case, using the artificial neural networkprocessing technique may improve an accuracy of the trained machinelearning model generated by the plan platform by being more robust tonoisy, imprecise, or incomplete data, and by enabling the plan platformto detect patterns and/or trends undetectable to human analysts orsystems using less complex techniques.

As shown in FIG. 1B, the user may utilize the user device to conducttransactions associated with the account of the user. For example, theuser may utilize the user device to conduct transactions that arerelated to the event (e.g., purchasing food, paying for lodging, and/orthe like) and/or may utilize the user device to conduct transactionsthat are not related to the event (e.g., purchasing a computer,purchasing exercise equipment, and/or the like). As further shown inFIG. 1B, and by reference number 110, the plan platform may receive,from the user device, transaction information identifying thetransactions associated with the account of the user. In someimplementations, the transaction information may include informationindicating a purchased item (e.g., a product, a service, and/or thelike), financial information associated with the purchase (e.g.,information indicating an amount charged to the user for the item, theaccount of the user that is charged for the item, an invoice number forthe item, a confirmation number for the item, and/or the like), and/orthe like.

In some implementations, the user device may include a transaction card(e.g., a credit card, a debit card, a gift card, a rewards card, and/orthe like) or the user may utilize a transaction card to conducttransactions associated with the account of the user. In suchimplementations, the transaction information may be received, by theplan platform, from a device other than the user device, such as from amerchant device associated with a merchant with which the user isconducting a transaction via the transaction card.

In some implementations, the plan platform may perform natural languageprocessing on the transaction information so that the transactioninformation is in a machine-readable format. For example, the planplatform may perform natural language processing on the transactioninformation to generate natural language processing results, and mayanalyze the natural language processing results to identify informationincluded in the transaction information. Natural language processinginvolves techniques performed (e.g., by a computer system) to analyze,understand, and derive meaning from human language in a useful way.Natural language processing can be applied to analyze text, allowingmachines to understand how humans speak, enabling real worldapplications such as automatic text summarization, sentiment analysis,topic extraction, named entity recognition, parts-of-speech tagging,relationship extraction, stemming, and/or the like.

As shown in FIG. 1C, and by reference number 115, the plan platform mayprocess the plan information and the transaction information, with amachine learning model, to identify transactions related to the plan. Insome implementations, the machine learning model may identify atransaction related to the plan when the transaction corresponds to oneor more of the plan items of the plan. For example, if the transactionrelates to buying dinner in a city and at a time associated with theplan, the machine learning model may identify the transaction as beingrelated to a food plan item of the plan. In another example, if thetransaction relates to buying lawn fertilizer, the machine learningmodel may not identify the transaction as being related to a plan itemof the plan.

In some implementations, the machine learning model may include apattern recognition model that identifies transactions related to theplan. In some implementations, the plan platform may perform a trainingoperation on the machine learning model with historical plan information(e.g., from prior plans of the user, from plans of other similar users,and/or the like) and historical transaction information (e.g., fromprior transactions of the user, from transactions of other similarusers, and/or the like), as described above in connection with FIG. 1A.

As shown in FIG. 1D, and by reference number 120, the plan platform maydetermine that a threshold preference for a plan item of the plan issatisfied based on one or more of the transactions related to the plan.For example, if the plan platform identifies three transactions thatrelate to an entertainment category of the plan (e.g., a movie thatcosts $50, a show that costs $200, and a sporting event that costs$150), the plan platform may determine that the three transactionssatisfy a threshold preference (e.g., a budget of $400 forentertainment) for the entertainment category of the plan. In someimplementations, the threshold preference may be set to less than anactual budget for a plan item of the plan (e.g., a threshold preferenceof $350 for entertainment with a total budget of $400 forentertainment).

In some implementations, the plan platform may determine that two ormore threshold preferences for plan items of the plan are satisfiedbased on one or more of the transactions related to the plan. In someimplementations, the plan platform may provide, to the user device,information identifying the plan item of the plan for which thethreshold preference is satisfied. For example, the plan platform mayprovide, to the user device, a notification (e.g., via email, a shortmessage service (SMS) message, a voice call, the application installedon the user device, and/or the like) alerting the user that thethreshold preference for the plan item of the plan is satisfied.

As shown in FIG. 1E, and by reference number 125, the plan platform mayprocess plan item information, the plan information, and the transactioninformation, with a machine learning model, to determine one or morerecommendations for the plan. In some implementations, the plan iteminformation may include information identifying the plan item of theplan for which the threshold preference is satisfied, a priorityassociated with the identified plan item, a preference associated withthe identified plan item, and/or the like. In some implementations, themachine learning model may determine one or more recommendations for theplan based on the plan item satisfying the threshold preference. Forexample, if the plan item satisfying the threshold relates to a foodcategory of the plan and an entertainment category of the plan has notsatisfied a threshold preference, the plan platform may recommendallocating funds from the entertainment category to the food category sothat the food category is below the threshold preference.

In some implementations, the machine learning model may include a Markovmodel, a Bayesian model, a Bayesian-Markov decision model, and/or thelike that determines one or more recommendations for the plan based onthe plan item information, the plan information, and the transactioninformation. In some implementations, the plan platform may perform atraining operation on the machine learning model with historical planinformation (e.g., from prior plans of the user, from plans of othersimilar users, and/or the like) and historical transaction information(e.g., from prior transactions of the user, from transactions of othersimilar users, and/or the like), as described above in connection withFIG. 1A.

In some implementations, the plan platform may utilize the machinelearning model to compare a priority associated with the plan item thatsatisfies the threshold preference and priorities associated with otherplan items of the plan. The plan platform may determine the one or morerecommendations based on comparing the priority associated with the planitem that satisfies the threshold preference and the prioritiesassociated with the other plan items of the plan. For example, if theplan item that satisfies the threshold preference includes a higherpriority than two other plan items, the plan platform may recommendallocating funds from the other two plan items so that the plan item isbelow the threshold preference.

In some implementations, the plan platform may utilize the machinelearning model to compare a preference associated with the plan itemthat satisfies the threshold preference and preferences associated withother plan items of the plan, other accounts of the user, informationidentifying merchants associated with the other plan items, informationidentifying products and/or services associated with the other planitems, and/or the like. The plan platform may determine the one or morerecommendations based on comparing the preference associated with theplan item that satisfies the threshold preference and the preferencesassociated with the other plan items of the plan, other accounts of theuser, information identifying merchants associated with the other planitems, information identifying products and/or services associated withthe other plan items, and/or the like. For example, if the plan itemthat satisfies the threshold preference is preferred over another planitem (e.g., indicates that the user prefers spending on the plan itemmore than the other plan item), the plan platform may recommendallocating funds from the other plan item so that the plan item is belowthe threshold preference, utilizing particular merchants for the otherplan items to save money, utilizing other products and/or services forthe other plan items to save money, and/or the like.

As shown in FIG. 1F, and by reference number 130, the plan platform mayprovide, to the user device, information indicating the one or morerecommendations. In some implementations, the user device may receivethe information indicating the one or more recommendations and maypresent the information to the user via a user interface. For example,the user interface may include information recommending that the userbuy food at a grocery store rather than restaurant (e.g., when a foodcategory of the plan satisfies a preference threshold), that the userpurchase fuel at a particular gas station (e.g., to save expenses for atransportation category of the plan), that the user spend less on anentertainment category of the plan and use the savings on a foodcategory of the plan, that the user prevent use of the account for aplan item, and/or the like.

As shown in FIG. 1G, and by reference number 135, the plan platform mayautomatically perform one or more actions based on the one or morerecommendations for the plan. In some implementations, the plan platformmay allot an amount of the plan to the plan item that satisfies thethreshold preference from a plan item that is not exceeded. In this way,the plan platform may ensure that the plan is maintained and that nothreshold preferences are satisfied. In some implementations, the planplatform may increase an amount allotted to the plan item that satisfiesthe threshold preference. In this way, the plan platform may ensure thatthe plan item does not exceed the threshold preference.

In some implementations, the plan platform may prevent futuretransactions associated with the plan item that satisfies the thresholdpreference. In this way, the plan platform may ensure that the plan itemdoes not exceed the threshold preference and that the plan ismaintained. In some implementations, the plan platform may requirethird-party approval of future transactions associated with the planitem that satisfies the threshold preference. In this way, the planplatform may ensure that the user does not perform any more transactionsthat causes the plan item to exceed the threshold preference.

In some implementations, the plan platform may provide directions tomerchants providing products and/or services associated with the planitem that satisfies the threshold preference. For example, the planplatform may recommend cheaper restaurants to the user and providedirections to the cheaper restaurants. In this way, the plan platformmay encourage the user to make wiser decisions with regard to the planitem.

In some implementations, the plan platform may provide the directions tothe merchants to an autonomous vehicle that may drive to the merchantsfor the user. For example, the autonomous vehicle may drive to a groceryto pick up food in order to prevent the user from spending money on arestaurant.

In some implementations, with permission from a third-party, the planplatform may allocate an amount from another account to the plan itemthat satisfies the threshold preference. In this way, the plan platformmay ensure that the plan item is being drawn from another account otherthan the account associated with the plan. In some implementations, theplan platform may adjust the plan item that satisfies the thresholdpreference for a future time period. For example, if the plan item is amonthly food plan item, the plan platform may recommend increasing anamount allocated to the monthly food plan item. In this way, the planplatform may ensure that the plan item does not exceed the thresholdpreference in the future.

In some implementations, the plan platform may research and identify anew account to hold cash reserves (e.g., identify an account that getsthe most interest) so that the user may earn interest on the cashreserves and use toward the plan. In some implementations, the planplatform may automatic fill out an application to open this new account.In some implementations, the plan platform may automatically move cashreserves among the user's existing accounts (e.g., move from an accountwith less interest to an account with more interest) so that user mayearn more interest on the cash reserves.

In some implementations, the plan platform may enable the user toidentify facts and assumptions (e.g., create scenarios) for the plan andallow the user to adjust the facts and assumptions to determine how theadjustments affect the plan. In some implementations, the plan platformmay forecast the plan out in the future for the facts and assumptions.For example, the plan platform may identify expenditures in a month, ayear, five years, and/or the like for the plan if the facts andassumptions are maintained, may recommend adjustments to the facts andassumptions and may identify savings in a month, a year, five years,and/or the like based on the adjustments, and/or the like.

In some implementations, the plan platform may automatically populateaccounting software with the transaction information and/or the planinformation so that use may utilize the accounting software to track theplan and transactions.

In some implementations, the plan platform may automatically generateperiodic reports for the plan, such as reports identifying past plans,future projections for the plan, alternative plans to replace the plan,and/or the like.

In some implementations, based on analyzing information associated withother users with of similar plans, the plan platform may generaterecommendations to prevent issues with the plan before the issues arise.For example, the plan platform may provide recommendations to avoidissues that arose in the other users' plans.

In this way, several different stages of the process for utilizingmachine learning models to automatically perform actions that maintain aplan for an event are automated, which may remove human subjectivity andwaste from the process, and which may improve speed and efficiency ofthe process and conserve computing resources (e.g., processingresources, memory resources, and/or the like). Furthermore,implementations described herein use a rigorous, computerized process toperform tasks or roles that were not previously performed or werepreviously performed using subjective human intuition or input. Forexample, currently there does not exist a technique that utilizesmachine learning models to automatically perform actions that maintain aplan for an event. Finally, automating the process for utilizing machinelearning models to automatically perform actions that maintain a planfor an event conserves computing resources (e.g., processing resources,memory resources, and/or the like) that would otherwise be wasted inattempting to perform actions that maintain a plan for an event.

As indicated above, FIGS. 1A-1G are provided merely as examples. Otherexamples are possible and may differ from what was described with regardto FIGS. 1A-1G.

FIG. 2 is a diagram of an example environment 200 in which systemsand/or methods, described herein, may be implemented. As shown in FIG.2, environment 200 may include a user device 210, a plan platform 220,and a network 230. Devices of environment 200 may interconnect via wiredconnections, wireless connections, or a combination of wired andwireless connections.

User device 210 includes one or more devices capable of receiving,generating, storing, processing, and/or providing information, such asinformation described herein. For example, user device 210 may include amobile phone (e.g., a smart phone, a radiotelephone, etc.), a laptopcomputer, a tablet computer, a desktop computer, a handheld computer, agaming device, a wearable communication device (e.g., a smartwristwatch, a pair of smart eyeglasses, etc.), or a similar type ofdevice. In some implementations, user device 210 may receive informationfrom and/or transmit information to plan platform 220.

Plan platform 220 includes one or more devices that utilize machinelearning models to automatically perform actions that maintain a planfor an event. In some implementations, plan platform 220 may be designedto be modular such that certain software components may be swapped in orout depending on a particular need. As such, plan platform 220 may beeasily and/or quickly reconfigured for different uses. In someimplementations, plan platform 220 may receive information from and/ortransmit information to one or more user devices 210.

In some implementations, as shown, plan platform 220 may be hosted in acloud computing environment 222. Notably, while implementationsdescribed herein describe plan platform 220 as being hosted in cloudcomputing environment 222, in some implementations, plan platform 220may not be cloud-based (i.e., may be implemented outside of a cloudcomputing environment) or may be partially cloud-based.

Cloud computing environment 222 includes an environment that hosts planplatform 220. Cloud computing environment 222 may provide computation,software, data access, storage, etc. services that do not requireend-user knowledge of a physical location and configuration of system(s)and/or device(s) that host plan platform 220. As shown, cloud computingenvironment 222 may include a group of computing resources 224 (referredto collectively as “computing resources 224” and individually as“computing resource 224”).

Computing resource 224 includes one or more personal computers,workstation computers, server devices, or other types of computationand/or communication devices. In some implementations, computingresource 224 may host plan platform 220. The cloud resources may includecompute instances executing in computing resource 224, storage devicesprovided in computing resource 224, data transfer devices provided bycomputing resource 224, etc. In some implementations, computing resource224 may communicate with other computing resources 224 via wiredconnections, wireless connections, or a combination of wired andwireless connections.

As further shown in FIG. 2, computing resource 224 includes a group ofcloud resources, such as one or more applications (“APPs”) 224-1, one ormore virtual machines (“VMs”) 224-2, virtualized storage (“VSs”) 224-3,one or more hypervisors (“HYPs”) 224-4, and/or the like.

Application 224-1 includes one or more software applications that may beprovided to or accessed by user device 210. Application 224-1 mayeliminate a need to install and execute the software applications onuser device 210. For example, application 224-1 may include softwareassociated with plan platform 220 and/or any other software capable ofbeing provided via cloud computing environment 222. In someimplementations, one application 224-1 may send/receive informationto/from one or more other applications 224-1, via virtual machine 224-2.

Virtual machine 224-2 includes a software implementation of a machine(e.g., a computer) that executes programs like a physical machine.Virtual machine 224-2 may be either a system virtual machine or aprocess virtual machine, depending upon use and degree of correspondenceto any real machine by virtual machine 224-2. A system virtual machinemay provide a complete system platform that supports execution of acomplete operating system (“OS”). A process virtual machine may executea single program, and may support a single process. In someimplementations, virtual machine 224-2 may execute on behalf of a user(e.g., a user of user device 210 or an operator of plan platform 220),and may manage infrastructure of cloud computing environment 222, suchas data management, synchronization, or long-duration data transfers.

Virtualized storage 224-3 includes one or more storage systems and/orone or more devices that use virtualization techniques within thestorage systems or devices of computing resource 224. In someimplementations, within the context of a storage system, types ofvirtualizations may include block virtualization and filevirtualization. Block virtualization may refer to abstraction (orseparation) of logical storage from physical storage so that the storagesystem may be accessed without regard to physical storage orheterogeneous structure. The separation may permit administrators of thestorage system flexibility in how the administrators manage storage forend users. File virtualization may eliminate dependencies between dataaccessed at a file level and a location where files are physicallystored. This may enable optimization of storage use, serverconsolidation, and/or performance of non-disruptive file migrations.

Hypervisor 224-4 may provide hardware virtualization techniques thatallow multiple operating systems (e.g., “guest operating systems”) toexecute concurrently on a host computer, such as computing resource 224.Hypervisor 224-4 may present a virtual operating platform to the guestoperating systems, and may manage the execution of the guest operatingsystems. Multiple instances of a variety of operating systems may sharevirtualized hardware resources.

Network 230 includes one or more wired and/or wireless networks. Forexample, network 230 may include a cellular network (e.g., a fifthgeneration (5G) network, a long-term evolution (LTE) network, a thirdgeneration (3G) network, a code division multiple access (CDMA) network,etc.), a public land mobile network (PLMN), a local area network (LAN),a wide area network (WAN), a metropolitan area network (MAN), atelephone network (e.g., the Public Switched Telephone Network (PSTN)),a private network, an ad hoc network, an intranet, the Internet, a fiberoptic-based network, and/or the like, and/or a combination of these orother types of networks.

The number and arrangement of devices and networks shown in FIG. 2 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 2. Furthermore, two or more devices shown in FIG. 2 may beimplemented within a single device, or a single device shown in FIG. 2may be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) ofenvironment 200 may perform one or more functions described as beingperformed by another set of devices of environment 200.

FIG. 3 is a diagram of example components of a device 300. Device 300may correspond to user device 210, plan platform 220, and/or computingresource 224. In some implementations, user device 210, plan platform220, and/or computing resource 224 may include one or more devices 300and/or one or more components of device 300. As shown in FIG. 3, device300 may include a bus 310, a processor 320, a memory 330, a storagecomponent 340, an input component 350, an output component 360, and acommunication interface 370.

Bus 310 includes a component that permits communication among thecomponents of device 300. Processor 320 is implemented in hardware,firmware, or a combination of hardware and software. Processor 320 is acentral processing unit (CPU), a graphics processing unit (GPU), anaccelerated processing unit (APU), a microprocessor, a microcontroller,a digital signal processor (DSP), a field-programmable gate array(FPGA), an application-specific integrated circuit (ASIC), or anothertype of processing component. In some implementations, processor 320includes one or more processors capable of being programmed to perform afunction. Memory 330 includes a random-access memory (RAM), a read onlymemory (ROM), and/or another type of dynamic or static storage device(e.g., a flash memory, a magnetic memory, and/or an optical memory) thatstores information and/or instructions for use by processor 320.

Storage component 340 stores information and/or software related to theoperation and use of device 300. For example, storage component 340 mayinclude a hard disk (e.g., a magnetic disk, an optical disk, amagneto-optic disk, and/or a solid-state disk), a compact disc (CD), adigital versatile disc (DVD), a floppy disk, a cartridge, a magnetictape, and/or another type of non-transitory computer-readable medium,along with a corresponding drive.

Input component 350 includes a component that permits device 300 toreceive information, such as via user input (e.g., a touch screendisplay, a keyboard, a keypad, a mouse, a button, a switch, and/or amicrophone). Additionally, or alternatively, input component 350 mayinclude a sensor for sensing information (e.g., a global positioningsystem (GPS) component, an accelerometer, a gyroscope, and/or anactuator). Output component 360 includes a component that providesoutput information from device 300 (e.g., a display, a speaker, and/orone or more light-emitting diodes (LEDs)).

Communication interface 370 includes a transceiver-like component (e.g.,a transceiver and/or a separate receiver and transmitter) that enablesdevice 300 to communicate with other devices, such as via a wiredconnection, a wireless connection, or a combination of wired andwireless connections. Communication interface 370 may permit device 300to receive information from another device and/or provide information toanother device. For example, communication interface 370 may include anEthernet interface, an optical interface, a coaxial interface, aninfrared interface, a radio frequency (RF) interface, a universal serialbus (USB) interface, a Wi-Fi interface, a cellular network interface,and/or the like.

Device 300 may perform one or more processes described herein. Device300 may perform these processes based on processor 320 executingsoftware instructions stored by a non-transitory computer-readablemedium, such as memory 330 and/or storage component 340. Acomputer-readable medium is defined herein as a non-transitory memorydevice. A memory device includes memory space within a single physicalstorage device or memory space spread across multiple physical storagedevices.

Software instructions may be read into memory 330 and/or storagecomponent 340 from another computer-readable medium or from anotherdevice via communication interface 370. When executed, softwareinstructions stored in memory 330 and/or storage component 340 may causeprocessor 320 to perform one or more processes described herein.Additionally, or alternatively, hardwired circuitry may be used in placeof or in combination with software instructions to perform one or moreprocesses described herein. Thus, implementations described herein arenot limited to any specific combination of hardware circuitry andsoftware.

The number and arrangement of components shown in FIG. 3 are provided asan example. In practice, device 300 may include additional components,fewer components, different components, or differently arrangedcomponents than those shown in FIG. 3. Additionally, or alternatively, aset of components (e.g., one or more components) of device 300 mayperform one or more functions described as being performed by anotherset of components of device 300.

FIG. 4 is a flow chart of an example process 400 for utilizing machinelearning models to automatically perform actions that maintain a planfor an event. In some implementations, one or more process blocks ofFIG. 4 may be performed by a plan platform (e.g., plan platform 220). Insome implementations, one or more process blocks of FIG. 4 may beperformed by another device or a group of devices separate from orincluding the plan platform, such as a user device (e.g., user device210).

As shown in FIG. 4, process 400 may include receiving plan informationidentifying a plan for an event, wherein the plan information includesinformation identifying an account associated with the plan, plan itemsof the plan, priorities associated with the plan items, and preferencesassociated with the plan items (block 410). For example, the planplatform (e.g., using computing resource 224, processor 320,communication interface 370, and/or the like) may receive planinformation identifying a plan for an event, as described above inconnection with FIGS. 1A-2. In some implementations, the planinformation may include information identifying an account associatedwith the plan, plan items of the plan, priorities associated with theplan items, and preferences associated with the plan items.

As further shown in FIG. 4, process 400 may include receiving, afterreceiving the plan information, transaction information identifyingtransactions associated with the account (block 420). For example, theplan platform (e.g., using computing resource 224, processor 320, memory330, communication interface 370, and/or the like) may receive, afterreceiving the plan information, transaction information identifyingtransactions associated with the account, as described above inconnection with FIGS. 1A-2.

As further shown in FIG. 4, process 400 may include processing the planinformation and the transaction information, with a first machinelearning model, to identify transactions related to the plan (block430). For example, the plan platform (e.g., using computing resource224, processor 320, storage component 340, and/or the like) may processthe plan information and the transaction information, with a firstmachine learning model, to identify transactions related to the plan, asdescribed above in connection with FIGS. 1A-2.

As further shown in FIG. 4, process 400 may include determining that athreshold preference for a particular plan item of the plan is satisfiedbased on one or more of the transactions related to the plan (block440). For example, the plan platform (e.g., using computing resource224, processor 320, memory 330, communication interface 370, and/or thelike) may determine that a threshold preference for a particular planitem of the plan is satisfied based on one or more of the transactionsrelated to the plan, as described above in connection with FIGS. 1A-2.

As further shown in FIG. 4, process 400 may include processinginformation associated with the particular plan item, the planinformation, and the transaction information, with a second machinelearning model, to determine one or more recommendations for the plan,wherein the information associated with the particular plan itemincludes information identifying a priority associated with theparticular plan item, and a preference associated with the particularplan item (block 450). For example, the plan platform (e.g., usingcomputing resource 224, processor 320, storage component 340, and/or thelike) may process information associated with the particular plan item,the plan information, and the transaction information, with a secondmachine learning model, to determine one or more recommendations for theplan, as described above in connection with FIGS. 1A-2. In someimplementations, the information associated with the particular planitem may include information identifying a priority associated with theparticular plan item, and a preference associated with the particularplan item.

As further shown in FIG. 4, process 400 may include automaticallyperforming one or more actions based on the one or more recommendationsfor the plan (block 460). For example, the plan platform (e.g., usingcomputing resource 224, processor 320, memory 330, communicationinterface 370, and/or the like) may automatically perform one or moreactions based on the one or more recommendations for the plan, asdescribed above in connection with FIGS. 1A-2.

Process 400 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or described with regard to any other process described herein.

In some implementations, when automatically performing the one or moreactions, the plan platform may allot an amount of the plan to theparticular plan item from another plan item of the plan, may increase anamount allotted to the particular plan item, may prevent futuretransactions associated with the particular plan item, may requirethird-party approval of the future transactions associated with theparticular plan item, may provide directions to recommended merchantsproviding products and/or services associated with the particular planitem, may allocate an amount from another account to the particular planitem, and/or may adjust the particular plan item for a future timeperiod.

In some implementations, the plan platform may provide informationindicating the one or more recommendations to a user device associatedwith a user of the plan. In some implementations, the first machinelearning model may include a pattern recognition model, and the secondmachine learning model may include one or more of a Markov model, aBayesian model, or a Bayesian-Markov decision model.

In some implementations, when processing the information associated withthe particular plan item, the plan information, and the transactioninformation, with the second machine learning model, to determine theone or more recommendations for the plan, the plan platform may utilizethe second machine learning model to compare the priority associatedwith the particular plan item and the priorities associated with theplan items other than the particular plan item, and may determine theone or more recommendations based on comparing the priority associatedwith the particular plan item and the priorities associated with theplan items other than the particular plan item.

In some implementations, when processing the information associated withthe particular plan item, the plan information, and the transactioninformation, with the second machine learning model, to determine theone or more recommendations for the plan, the plan platform may utilizethe second machine learning model to compare the preference associatedwith the particular plan item and the preferences associated with theplan items other than the particular plan item, and determine the one ormore recommendations based on comparing the preference associated withthe particular plan item and the preferences associated with the planitems other than the particular plan item.

In some implementations, the plan platform may process the planinformation, with a third machine learning model, to identifyrecommended plan items, recommended priorities for the recommended planitems, and recommended preferences for the recommended plan items, andmay provide the recommended plan items, the recommended priorities, andthe recommended preferences to the user device.

Although FIG. 4 shows example blocks of process 400, in someimplementations, process 400 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 4. Additionally, or alternatively, two or more of theblocks of process 400 may be performed in parallel.

FIG. 5 is a flow chart of an example process 500 for utilizing machinelearning models to automatically perform actions that maintain a planfor an event. In some implementations, one or more process blocks ofFIG. 5 may be performed by a plan platform (e.g., plan platform 220). Insome implementations, one or more process blocks of FIG. 5 may beperformed by another device or a group of devices separate from orincluding the plan platform, such as a user device (e.g., user device210).

As shown in FIG. 5, process 500 may include receiving, from a userdevice, plan information identifying a plan for an event, wherein theplan information includes information identifying an account associatedwith the plan, plan items of the plan, priorities associated with theplan items, and preferences associated with the plan items, and whereinthe user device is associated with a user of the account and the plan(block 510). For example, the plan platform (e.g., using computingresource 224, processor 320, communication interface 370, and/or thelike) may receive, from a user device, plan information identifying aplan for an event, as described above in connection with FIGS. 1A-2. Insome implementations, the plan information may include informationidentifying an account associated with the plan, plan items of the plan,priorities associated with the plan items, and preferences associatedwith the plan items. In some implementations, the user device may beassociated with a user of the account and the plan.

As further shown in FIG. 5, process 500 may include receiving, afterreceiving the plan information, transaction information identifyingtransactions associated with the account (block 520). For example, theplan platform (e.g., using computing resource 224, processor 320,storage component 340, and/or the like) may receive, after receiving theplan information, transaction information identifying transactionsassociated with the account, as described above in connection with FIGS.1A-2.

As further shown in FIG. 5, process 500 may include processing the planinformation and the transaction information, with a first model, toidentify transactions related to the plan (block 530). For example, theplan platform (e.g., using computing resource 224, processor 320, memory330, and/or the like) may process the plan information and thetransaction information, with a first model, to identify transactionsrelated to the plan, as described above in connection with FIGS. 1A-2.

As further shown in FIG. 5, process 500 may include determining that athreshold preference for a particular plan item of the plan is satisfiedbased on one or more of the transactions related to the plan (block540). For example, the plan platform (e.g., using computing resource224, processor 320, storage component 340, and/or the like) maydetermine that a threshold preference for a particular plan item of theplan is satisfied based on one or more of the transactions related tothe plan, as described above in connection with FIGS. 1A-2.

As further shown in FIG. 5, process 500 may include processinginformation associated with the particular plan item, the planinformation, and the transaction information, with a second model, todetermine one or more recommendations for the plan, wherein theinformation associated with the particular plan item includesinformation identifying a priority associated with the particular planitem, and a preference associated with the particular plan item (block550). For example, the plan platform (e.g., using computing resource224, processor 320, memory 330, and/or the like) may process informationassociated with the particular plan item, the plan information, and thetransaction information, with a second model, to determine one or morerecommendations for the plan, as described above in connection withFIGS. 1A-2. In some implementations, the information associated with theparticular plan item may include information identifying a priorityassociated with the particular plan item, and a preference associatedwith the particular plan item.

As further shown in FIG. 5, process 500 may include providinginformation indicating the one or more recommendations to the userdevice (block 560). For example, the plan platform (e.g., usingcomputing resource 224, processor 320, memory 330, communicationinterface 370, and/or the like) may provide information indicating theone or more recommendations to the user device, as described above inconnection with FIGS. 1A-2.

Process 500 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or described with regard to any other process described herein.

In some implementations, the plan platform may automatically perform oneor more actions based on the one or more recommendations for the plan.In some implementations, when automatically performing the one or moreactions, the plan platform may allot an amount of the plan to theparticular plan item from another plan item of the plan, may increase anamount allotted to the particular plan item, may prevent futuretransactions associated with the particular plan item, may requirethird-party approval of the future transactions associated with theparticular plan item, may provide directions to recommended merchantsproviding products and/or services associated with the particular planitem, may allocate an amount from another account to the particular planitem, and/or may adjust the particular plan item for a future timeperiod.

In some implementations, the plan platform may provide, to the userdevice, information indicating that the particular plan item satisfiesthe threshold preference. In some implementations, when processing theinformation associated with the particular plan item, the planinformation, and the transaction information, with the second model, todetermine the one or more recommendations for the plan, the planplatform may utilize the second model to compare the priority associatedwith the particular plan item and the priorities associated with theplan items other than the particular plan item, and may determine theone or more recommendations based on comparing the priority associatedwith the particular plan item and the priorities associated with theplan items other than the particular plan item.

In some implementations, when processing the information associated withthe particular plan item, the plan information, and the transactioninformation, with the second model, to determine the one or morerecommendations for the plan, the plan platform may utilize the secondmodel to compare the preference associated with the particular plan itemand the preferences associated with the plan items other than theparticular plan item, and may determine the one or more recommendationsbased on comparing the preference associated with the particular planitem and the preferences associated with the plan items other than theparticular plan item.

In some implementations, the plan may be associated with a recurringtime period and the plan platform may disregard, after expiration of therecurring time period, the transactions related to the plan, mayreceive, after expiration of the recurring time period, new transactioninformation identifying new transactions associated with the account,and may process the plan information and the new transactioninformation, with the first model, to identify new transactions relatedto the plan

Although FIG. 5 shows example blocks of process 500, in someimplementations, process 500 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 5. Additionally, or alternatively, two or more of theblocks of process 500 may be performed in parallel.

FIG. 6 is a flow chart of an example process 600 for utilizing machinelearning models to automatically perform actions that maintain a planfor an event. In some implementations, one or more process blocks ofFIG. 6 may be performed by a plan platform (e.g., plan platform 220). Insome implementations, one or more process blocks of FIG. 6 may beperformed by another device or a group of devices separate from orincluding the plan platform, such as a user device (e.g., user device210).

As shown in FIG. 6, process 600 may include receiving plan informationidentifying a plan for an event, wherein the plan information includesinformation identifying an account associated with the plan, plan itemsof the plan, priorities associated with the plan items, and preferencesassociated with the plan items (block 610). For example, the planplatform (e.g., using computing resource 224, processor 320,communication interface 370, and/or the like) may receive planinformation identifying a plan for an event, as described above inconnection with FIGS. 1A-2. In some implementations, the planinformation may include information identifying an account associatedwith the plan, plan items of the plan, priorities associated with theplan items, and preferences associated with the plan items.

As further shown in FIG. 6, process 600 may include receiving, afterreceiving the plan information, transaction information identifyingtransactions associated with the account (block 620). For example, theplan platform (e.g., using computing resource 224, processor 320,storage component 340, communication interface 370, and/or the like) mayreceive, after receiving the plan information, transaction informationidentifying transactions associated with the account, as described abovein connection with FIGS. 1A-2.

As further shown in FIG. 6, process 600 may include processing the planinformation and the transaction information, with a first machinelearning model, to identify transactions related to the plan (block630). For example, the plan platform (e.g., using computing resource224, processor 320, memory 330, and/or the like) may process the planinformation and the transaction information, with a first machinelearning model, to identify transactions related to the plan, asdescribed above in connection with FIGS. 1A-2.

As further shown in FIG. 6, process 600 may include determining that athreshold preference for a particular plan item of the plan is satisfiedbased on one or more of the transactions related to the plan (block640). For example, the plan platform (e.g., using computing resource224, processor 320, storage component 340, and/or the like) maydetermine that a threshold preference for a particular plan item of theplan is satisfied based on one or more of the transactions related tothe plan, as described above in connection with FIGS. 1A-2.

As further shown in FIG. 6, process 600 may include providing, to a userdevice associated with a user of the plan, an alert indicating that theparticular plan item satisfies the threshold preference (block 650). Forexample, the plan platform (e.g., using computing resource 224,processor 320, memory 330, communication interface 370, and/or the like)may provide, to a user device associated with a user of the plan, analert indicating that the particular plan item satisfies the thresholdpreference, as described above in connection with FIGS. 1A-2.

As further shown in FIG. 6, process 600 may include processinginformation associated with the particular plan item, the planinformation, and the transaction information, with a second machinelearning model, to determine one or more recommendations for the plan,wherein the information associated with the particular plan itemincludes information identifying a priority associated with theparticular plan item, and a preference associated with the particularplan item (block 660). For example, the plan platform (e.g., usingcomputing resource 224, processor 320, memory 330, and/or the like) mayprocess information associated with the particular plan item, the planinformation, and the transaction information, with a second machinelearning model, to determine one or more recommendations for the plan,as described above in connection with FIGS. 1A-2. In someimplementations, the information associated with the particular planitem may include information identifying a priority associated with theparticular plan item, and a preference associated with the particularplan item.

As further shown in FIG. 6, process 600 may include providing, to theuser device, information indicating the one or more recommendations(block 670). For example, the plan platform (e.g., using computingresource 224, processor 320, communication interface 370, and/or thelike) may provide, to the user device, information indicating the one ormore recommendations, as described above in connection with FIGS. 1A-2.

Process 600 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or described with regard to any other process described herein.

In some implementations, the plan platform may automatically perform oneor more actions based on the one or more recommendations for the plan.In some implementations, when automatically performing the one or moreactions, the plan platform may allot an amount of the plan to theparticular plan item from another plan item of the plan, may increase anamount allotted to the particular plan item, may prevent futuretransactions associated with the particular plan item, may requirethird-party approval of the future transactions associated with theparticular plan item, may provide directions to recommended merchantsproviding products and/or services associated with the particular planitem, may allocate an amount from another account to the particular planitem, and/or may adjust the particular plan item for a future timeperiod.

In some implementations, when processing the information associated withthe particular plan item, the plan information, and the transactioninformation, with the second machine learning model, to determine theone or more recommendations for the plan, the plan platform may utilizethe second machine learning model to compare the priority associatedwith the particular plan item and the priorities associated with theplan items other than the particular plan item, and may determine theone or more recommendations based on comparing the priority associatedwith the particular plan item and the priorities associated with theplan items other than the particular plan item.

In some implementations, when processing the information associated withthe particular plan item, the plan information, and the transactioninformation, with the second machine learning model, to determine theone or more recommendations for the plan, the plan platform may utilizethe second machine learning model to compare the preference associatedwith the particular plan item and the preferences associated with theplan items other than the particular plan item, and may determine theone or more recommendations based on comparing the preference associatedwith the particular plan item and the preferences associated with theplan items other than the particular plan item. In some implementations,the account may be associated with one or more of a credit card, a debitcard, a gift card, or a rewards card.

Although FIG. 6 shows example blocks of process 600, in someimplementations, process 600 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 6. Additionally, or alternatively, two or more of theblocks of process 600 may be performed in parallel.

The foregoing disclosure provides illustration and description, but isnot intended to be exhaustive or to limit the implementations to theprecise form disclosed. Modifications and variations are possible inlight of the above disclosure or may be acquired from practice of theimplementations.

As used herein, the term component is intended to be broadly construedas hardware, firmware, or a combination of hardware and software.

Certain user interfaces have been described herein and/or shown in thefigures. A user interface may include a graphical user interface, anon-graphical user interface, a text-based user interface, or the like.A user interface may provide information for display. In someimplementations, a user may interact with the information, such as byproviding input via an input component of a device that provides theuser interface for display. In some implementations, a user interfacemay be configurable by a device and/or a user (e.g., a user may changethe size of the user interface, information provided via the userinterface, a position of information provided via the user interface,etc.). Additionally, or alternatively, a user interface may bepre-configured to a standard configuration, a specific configurationbased on a type of device on which the user interface is displayed,and/or a set of configurations based on capabilities and/orspecifications associated with a device on which the user interface isdisplayed.

It will be apparent that systems and/or methods, described herein, maybe implemented in different forms of hardware, firmware, or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods were described herein without reference tospecific software code—it being understood that software and hardwaremay be designed to implement the systems and/or methods based on thedescription herein.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of possible implementations. In fact,many of these features may be combined in ways not specifically recitedin the claims and/or disclosed in the specification. Although eachdependent claim listed below may directly depend on only one claim, thedisclosure of possible implementations includes each dependent claim incombination with every other claim in the claim set.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items(e.g., related items, unrelated items, a combination of related andunrelated items, etc.), and may be used interchangeably with “one ormore.” Where only one item is intended, the term “one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

1-20. (canceled)
 21. A method, comprising: processing, by a device andwith a machine learning model, plan information, identifying an itemassociated with a plan, and transaction information, identifying one ormore transactions, to identify a first transaction, of the one or moretransactions, as being related to the plan; determining, by the deviceand based on the first transaction, that a threshold preference,associated with the item, is satisfied; processing, by the device andbased on determining that the threshold preference is satisfied, iteminformation associated with the item, the plan information, and thetransaction information to determine one or more recommendations for theplan; and automatically performing, by the device and based on the oneor more recommendations for the plan, one or more actions for the plan.22. The method of claim 21, further comprising: receiving user inputassociated with at least one of the plan information or the transactioninformation, wherein processing the plan information and the transactioninformation is based on receiving the user input associated with the atleast one of the plan information or the transaction information. 23.The method of claim 21, further comprising: identifying, based onidentifying the first transaction as being related to the plan, thefirst transaction as being related to the item, wherein determining thatthe threshold preference is satisfied is based on identifying the firsttransaction as being related to the item.
 24. The method of claim 21,further comprising: processing, with an initial machine learning model,the plan information to identify one or more initial recommendationsassociated with the item, wherein processing, with the machine learningmodel, the plan information to identify the first transaction is basedon processing, with the initial machine learning model, to identify theone or more initial recommendations associated with the item.
 25. Themethod of claim 24, wherein the one or more initial recommendationsassociated with the item includes at least one: a recommended item thatis the item, a recommended priority of the item, or a recommendedpreference of the item.
 26. The method of claim 21, wherein processingthe item information, the plan information, and the transactioninformation to determine the one or more recommendations for the plancomprises: comparing, based on determining that the threshold preferenceis satisfied and using the machine learning model, a priority,associated with the item, to a priority associated with one or moreother items associated with the plan; and determining, based oncomparing the priority, associated with the item, to the priorityassociated with one or more other items, the one or more recommendationsfor the plan.
 27. The method of claim 21, wherein processing the iteminformation, the plan information, and the transaction information todetermine the one or more recommendations for the plan comprises:comparing, based on determining that the threshold preference issatisfied and using the machine learning model, a preference, associatedwith the item, to a preference associated with one or more other itemsassociated with the plan; and determining, based on comparing thepreference, associated with the item, to the preference associated withone or more other items, the one or more recommendation for the plan.28. The method of claim 21, wherein the one or more recommendationsrelate to a different item of the plan.
 29. The method of claim 21,further comprising: providing information indicating the one or morerecommendations to a user device associated with a user associated withthe plan.
 30. The method of claim 21, wherein automatically performingthe one or more actions includes one or more of: increasing an amountallotted to the item; preventing future transactions associated with theitem; requiring third-party approval of the future transactionsassociated with the item; providing directions to recommended merchantsproviding products and/or services associated with the item; allocatingan amount from another account to the item; or adjusting the item for afuture time period.
 31. A device, comprising: one or more memories; andone or more processors, coupled to the one or more memories, configuredto: process, by a device and with a machine learning model, planinformation, identifying an item associated with a plan, and transactioninformation, identifying one or more transactions, to identify a firsttransaction, of the one or more transactions, as being related to theplan; identify, based on identifying the first transaction as beingrelated to the plan, the first transaction as being related to the item,determine, based on identifying the first transaction, that a thresholdpreference, associated with the item, is satisfied; process, based ondetermining that the threshold preference is satisfied, item informationassociated with the item, the plan information, and the transactioninformation to determine one or more recommendations for the plan; andautomatically perform, based on the one or more recommendations for theplan, one or more actions for the plan.
 32. The device of claim 35,wherein the one or more processors are further configured to: receiveuser input associated with at least one of the plan information or thetransaction information, wherein processing the plan information and thetransaction information is based on receiving the user input associatedwith the at least one of the plan information or the transactioninformation.
 33. The device of claim 31, wherein the one or moreprocessors are further configured to: process, with an initial machinelearning model, the plan information to identify one or morerecommendations associated with the item, wherein processing, with themachine learning model, the plan information to identify the firsttransaction is based on processing, with the initial machine learningmodel, to identify the one or more recommendations associated with theitem.
 34. The device of claim 31, wherein the one or more processors, toprocess the item information, the plan information, and the transactioninformation to determine the one or more recommendations for the plan,are configured to: compare, based on determining that the thresholdpreference is satisfied and using the machine learning model, apriority, associated with the item, to a priority associated with one ormore other items associated with the plan, or compare, based ondetermining that the threshold preference is satisfied and using themachine learning model, a preference, associated with the item, to apreference associated with the one or more other items, and determine,based on comparing the priority associated with the item to the priorityassociated with the one or more other items or based on comparing thepreference associated with the item to the preference associated withone or more other items, the one or more recommendations for the plan.35. A non-transitory computer-readable medium storing a set ofinstructions, the set of instructions comprising: one or moreinstructions that, when executed by one or more processors of a device,cause the device to: receive, by the device, user input associated withplan information, identifying an item associated with a plan, andtransaction information identifying one or more transactions; process,by the device and with a machine learning model, the plan informationand the transaction information to identify a first transaction, of theone or more transactions, as being related to the plan; determine, basedon identifying the first transaction, that a threshold preference,associated with the item, is satisfied; process, based on determiningthat the threshold preference is satisfied, item information associatedwith the item, the plan information, and the transaction information todetermine one or more recommendations for the plan; and automaticallyperform, based on the one or more recommendations for the plan, one ormore actions for the plan.
 36. The non-transitory computer-readablemedium of claim 35, wherein the one or more instructions further causethe device to: identify, based on identifying the first transaction asbeing related to the plan, the first transaction as being related to theitem, wherein determining that the threshold preference is satisfied isbased on identifying the first transaction as being related to the item.37. The non-transitory computer-readable medium of claim 35, wherein theone or more instructions further cause the device to: process, with aninitial machine learning model, the plan information to identify one ormore initial recommendations associated with the item, whereinprocessing, with the machine learning model, the plan information toidentify the first transaction is based on processing, with the initialmachine learning model, to identify the one or more initialrecommendations associated with the item.
 38. The non-transitorycomputer-readable medium of claim 35, wherein the one or moreinstructions, that cause the device to process the item information, theplan information, and the transaction information, cause the device to:compare, based on determining that the threshold preference is satisfiedand using the machine learning model, a priority, associated with theitem, to a priority associated with one or more other items associatedwith the plan, or compare, based on determining that the thresholdpreference is satisfied and using the machine learning model, apreference, associated with the item, to a preference associated withthe one or more other items, and determine, based on comparing thepriority associated with the item to the priority associated with theone or more other items or based on comparing the preference associatedwith the item to the preference associated with one or more other items,the one or more recommendations for the plan.
 39. The non-transitorycomputer-readable medium of claim 35, wherein the one or morerecommendations associated with the item includes at least one: arecommended item that is the item, a recommended priority of the item,or a recommended preference of the item.
 40. The non-transitorycomputer-readable medium of claim 35, wherein the one or moreinstructions, that cause the device to automatically perform the one ormore actions, cause the device to: allot an amount of the plan to theitem from another item of the plan, increase an amount allotted to theitem, prevent future transactions associated with the item, requirethird-party approval of the future transactions associated with theitem, provide directions to recommended merchants providing productsand/or services associated with the item, allocate an amount fromanother account to the item, or adjust the item for a future timeperiod.